你的 AI 身份与 AI 团队,可携带至任意 AI 平台。
登录 立即开始
菜单
创建 Agent of Me 探索风格 专业 Agent 社区 Agents 排行榜 AI 资讯
AI 平台 目录 模型矩阵 对比 我该用哪个 AI? 集成指南 设置 OpenClaw Prompt 匹配度
学习与工具 学习 数据问答 Agent 构建器 API
关于 关于我们 联系我们 免责声明
登录 立即开始
账号
你的 AI 身份,随时可携带

注册免费账号以构建你的档案。默认私密,除非你主动发布,否则不会分享任何内容。

立即开始 登录
深色模式

🧭 引导视图
对 prompt、系统指令、上下文窗口、token 感到陌生?我们在浏览过程中以通俗语言解释每一个术语,内置于相同页面中,无需额外跳转。

⚡ 专业视图
你已经懂得如何写 prompt。只给核心内容,简洁紧凑,无多余说明。这是默认视图。

界面语言

Prompt Foundations · 章节 1/5, How AI Reads Your Words

学习目标
点击"下一步"(或使用方向键)逐条浏览。没有计时器,5 题测验在最后等你。顶部的 ← 随时可退出,进度自动保存。

It completes; it doesn't comprehend your intent

A language model does one thing extraordinarily well: given everything written so far, it produces the most plausible continuation. When you prompt it, you are not filing a request with an assistant who knows you. You are setting up a situation and letting the model continue it.

This explains the most common disappointment: you knew what you meant, but the model only had what you wrote. If your words fit a hundred different intents, you get the average of those hundred intents, which reads as generic.

Everything it knows about you is in the conversation

Unless a platform has stored instructions or memory for you, the model starts every conversation knowing nothing about your job, your project, your standards, or your taste. It is not being difficult; the information genuinely is not there.

The practical rule: anything that would change the answer belongs in the prompt. Your role, your reader, your deadline reality, the format you need, if it matters, it must be written down. This is exactly the problem persistent profiles solve: writing the stable facts once instead of every time.

Words are instructions, examples are stronger instructions

Every part of your prompt steers the continuation: the words you choose, the tone you write in, even your formatting. Write sloppily and you have quietly asked for a casual register. Write a numbered list and you have hinted the answer should be structured.

This is why showing beats telling. One example of the output you want often outperforms a paragraph describing it, because the model continues patterns more reliably than it interprets descriptions.

Order and emphasis matter

Models pay attention to the whole prompt, but instructions land harder when they are explicit, near the task, and not buried in the middle of a long paragraph. A constraint whispered in passing ('oh and keep it short') competes with everything else you wrote.

Put the task up front, the constraints where they are unmissable, and repeat the one non-negotiable at the end if the prompt is long. Redundancy for the thing you care most about is not bad style. It is good engineering.

小测验, How AI Reads Your Words

5 道题,每次从题库随机抽取。及格线 60%,可无限次重考。

下一节: The Anatomy of a Clear Ask →
阅读完整课文

1. It completes; it doesn't comprehend your intent

A language model does one thing extraordinarily well: given everything written so far, it produces the most plausible continuation. When you prompt it, you are not filing a request with an assistant who knows you. You are setting up a situation and letting the model continue it.

This explains the most common disappointment: you knew what you meant, but the model only had what you wrote. If your words fit a hundred different intents, you get the average of those hundred intents, which reads as generic.

2. Everything it knows about you is in the conversation

Unless a platform has stored instructions or memory for you, the model starts every conversation knowing nothing about your job, your project, your standards, or your taste. It is not being difficult; the information genuinely is not there.

The practical rule: anything that would change the answer belongs in the prompt. Your role, your reader, your deadline reality, the format you need, if it matters, it must be written down. This is exactly the problem persistent profiles solve: writing the stable facts once instead of every time.

3. Words are instructions, examples are stronger instructions

Every part of your prompt steers the continuation: the words you choose, the tone you write in, even your formatting. Write sloppily and you have quietly asked for a casual register. Write a numbered list and you have hinted the answer should be structured.

This is why showing beats telling. One example of the output you want often outperforms a paragraph describing it, because the model continues patterns more reliably than it interprets descriptions.

4. Order and emphasis matter

Models pay attention to the whole prompt, but instructions land harder when they are explicit, near the task, and not buried in the middle of a long paragraph. A constraint whispered in passing ('oh and keep it short') competes with everything else you wrote.

Put the task up front, the constraints where they are unmissable, and repeat the one non-negotiable at the end if the prompt is long. Redundancy for the thing you care most about is not bad style. It is good engineering.

商业

Business AnalystChief of StaffExecutive AssistantM&A AnalystManagement ConsultantOperations AnalystProject ManagerRecruiter

金融

AccountantDue Diligence AnalystEquity Research AnalystFamily Office AnalystFinancial AnalystFixed Income AnalystInvestment Banking AnalystPortfolio Analyst

法律

Contract Review AssistantLegal Due Diligence AssistantLegal Research AssistantParalegal

营销

Brand StrategistContent StrategistGEO AnalystMarketing StrategistSEO AnalystSales Strategist

个人

Career CoachLearning TutorReflection AssistantResearch AssistantTravel PlannerWriting Assistant

房地产

Acquisition AnalystAsset Management AnalystCommercial Real Estate AnalystDevelopment AnalystLease AnalystProperty Financial Analyst

研究

Competitive Intelligence AnalystDeep Research AnalystIndustry Research AnalystJournalist ResearcherMarket Research AnalystMedical Research Assistant

科技

AI Strategy AdvisorCybersecurity Research AssistantData AnalystProduct ManagerSoftware Engineer